---
title: "HL's Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "twitter", "facebook", "menu"]
    source_code: embed
---

```{r setup, include=FALSE}
#設定環境 如果無法設定套件環境，請下載套件，利用install.packages("XXX")指令
library(flexdashboard)
library(knitr)
library(DT)
library(rpivotTable)
library(ggplot2)
library(plotly)
library(dplyr)
library(openintro)
library(highcharter)
library(ggvis)

# 當圖形帶有中文，請安裝此套件
knitr::opts_chunk$set(fig.showtext=TRUE)
library(showtext)
showtext_auto()
```

```{r}
#帶入資料
data <- read.csv("./vehicle.csv")
```

```{r}
#預先指定顏色
mycolors <- c("blue", "#FFC125", "darkgreen", "darkorange")
```

Interactive Data Visualization
=====================================

Row
-------------------------------------

### Car Failure Analysis

```{r}
#顯示文字方塊
valueBox(paste("Failure"),
         color = "warning")
```

### Car Failures in US

```{r}
#顯示文字方塊，帶入數值
valueBox(length(data$State),
         icon = "fa-user")
```

### **Labor Cost**

```{r}
#顯示計量表，並指定顏色範圍。lc代表工資labor cost。
gauge(round(mean(data$lc),
            digits = 2),
            min = 0,
            max = 350,
            gaugeSectors(success = c(0, 150),
                         warning = c(150, 300),
                         danger = c(300, 450),
                         colors = c("green", "yellow", "red")))
```

### Massachusetts

```{r}
#顯示文字方塊，帶入特定州別條件下的數值
valueBox(sum(data$State == "MA"),
         icon = 'fa-building')
```

### California

```{r}
#顯示文字方塊，帶入特定州別條件下的數值
valueBox(sum(data$State == "CA"),
         icon = 'fa-building')
```

### Texas

```{r}
#顯示文字方塊，帶入特定州別條件下的數值
valueBox(sum(data$State == "TX"),
         icon = 'fa-building')
```

### Florida

```{r}
#顯示文字方塊，帶入特定州別條件下的數值
valueBox(sum(data$State == "FL"),
         icon = 'fa-building')
```

Row
-------------------------------

### Failures By State

```{r}
#繪製各州別的汽車故障總數長條圖
p1 <- data %>%
         group_by(State) %>%          #依州別分群計算
         summarise(count = n()) %>%   #計算總數
         plot_ly(x = ~reorder(State,count,desc),     #X軸為州別，並依照數量遞減排序
                 y = ~count,          #Y軸為州別
                 color = "red",       #顏色為紅色
                 type = 'bar') %>%    #圖形類別為長條圖
layout(xaxis = list(title = "Failures By State"),   #X軸的名稱
yaxis = list(title = 'Count'))                      #Y軸的名稱
p1   #呼叫圖形
```

### Top States

```{r}
#繪製汽車故障總數大於50的州佔比甜甜圈圖
p2 <- data %>%
         group_by(State) %>%
         summarise(count = n()) %>%
         filter(count>50) %>%    # 篩選總數大於50
         plot_ly(labels = ~State,
                 values = ~count,
                 marker = list(colors = mycolors)) %>%  #指定顏色
         add_pie(hole = 0.5) %>%  #中間空圈大小
         layout(xaxis = list(zeroline = F,
                             showline = F,
                             showticklabels = F,
                             showgrid = F),
                yaxis = list(zeroline = F,
                             showline = F,
                             showticklabels=F,
                             showgrid=F))
p2
```

### FM Vs Mileage

```{r}
#繪製汽車故障車齡(fm)與總里程(Mileage)的長條圖
p3 <- plot_ly(data,
              x = ~fm,
              y = ~Mileage,
              text = paste("FM:", data$fm, #註解裡要放的資訊
                           "Mileage:",
                           data$Mileage),
              type = "bar") %>%
         layout(xaxis = list(title="FM"),
                yaxis = list(title = "Failure Mileage"))
p3
```

Row
------------------------------------
### Scatter Plot of Month Vs Mileage

```{r}
#繪製汽車故障車齡(fm)與總里程(Mileage)的點陣圖與趨勢線
p4 <- plot_ly(data, x=~fm) %>%
         add_markers(y = ~Mileage,
                     text = ~paste("Mileage: ", Mileage),
                     showlegend = F) %>%
         add_lines(y = ~fitted(loess(Mileage ~ fm)), #增加趨勢線
                   name = "Loess Smoother",
                   color = I("#FFC125"),
                   showlegend = T,
                   line = list(width=5)) %>%
         layout(xaxis = list(title = "Month"),
                yaxis = list(title = "Mileage"))
p4
```

### Box Plot of Top State

```{r}
#繪製汽車故障州別(State)與工資(lc)的盒狀圖
data %>%
         group_by(State) %>%
         ggvis(~State, ~lc, fill = ~State) %>%
         layer_boxplots()
```

Map
========================================

### Map

```{r}
#繪製汽車故障州別(State)的地圖
car <- data %>%
         group_by(State) %>%
         summarize(total = n())
car$State <- abbr2state(car$State)

highchart() %>%
         hc_title(text = "Car Failures in US") %>%
         hc_subtitle(text = "Source: Vehiclefailure.csv") %>%
         hc_add_series_map(usgeojson, car,
                           name = "State",
                           value = "total",
                           joinBy = c("woename", "State")) %>%
         hc_mapNavigation(enabled = T)
```

Data Table
========================================

```{r}
#繪製汽車故障州別(State)的列表
datatable(data,
          caption = "Failure Data",
          rownames = T,
          filter = "top",
          options = list(pageLength = 25))
```

Pivot Table
=========================================

```{r}
#繪製汽車故障州別(State)的互動式樞紐分析圖
rpivotTable(data,
            aggregatorName = "Count",
            cols= "fm",
            rows = "State",
            rendererName = "Heatmap")
```

Summary {data-orientation=columns} 
===========================================

Column 
-----------------------------------

### Max Failure Month

```{r}
#車輛故障的最大車齡統計類別
valueBox(max(data$fm),
         icon = "fa-user" )
```

### Average Labor cost
```{r}
#車輛故障的平均工資齡統計類別
valueBox(round(mean(data$lc),
               digits = 2),
         icon = "fa-area-chart")
```

### Average Mileage at Failure

```{r}
#車輛故障的平均總里程統計類別
valueBox(round(mean(data$Mileage), digits = 2),
         icon = "fa-area-chart")
```

Column
---------------------------

Report

* This is a report on `r length(data$fm)` car failures.

* The average labor cost was `r mean(data$lc)`.

* The average material cost was `r mean(data$mc)`.

This report was generated on `r format(Sys.Date(), format = "%B %d, %Y")`.

About Report
========================================

Created by: Dashboard Viz by CMUH BDC

Confidential: HIGHLY!